class_docstr: Linear warmup and then cosine decay.
  Based on https://huggingface.co/ implementation.
class_docstr_tok: ["Linear","warmup","and","then","cosine","decay",".","Based","on","https",":","\/\/huggingface.co\/","implementation","."]
code: def lr_lambda(self, step):
      if step < self.warmup_steps:
          f = float(step) / float(max(1.0, self.warmup_steps))
          return (
              self.warmup_multiplier + (1 - self.warmup_multiplier) * f
          )
      progress = float(step - self.warmup_steps) / float(
          max(1, self.t_total - self.warmup_steps)
      )
      return max(
          0.0,
          0.5
          * (
              1.0
              + math.cos(math.pi * float(self.cycles) * 2.0 * progress)
          ),
      )
  
file_path: MONAI/monai/optimizers/lr_scheduler.py
imports: from __future__ import annotations
  import math
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
label_desc: Use image node_id 2 for calling the WarmupCosineSchedule obj's underlying member method code with example usage: obj.lr_lambda(step) and returns: max, unknown